Figure is intentionally designing its robots to avoid two extremes: menacing appearances and overly friendly looks with "googly eyes." The goal is to position the humanoid as a sophisticated, high-end piece of technology—a tool for humanity—rather than trying to fool users into thinking it's a toy or a person.

The humanoid form factor presents significant safety hazards in a home, such as a heavy robot becoming a “ballistic missile” if it falls down stairs. Simpler, specialized, low-mass designs are far more cost-effective and safer for domestic environments.

Progress in robotics for household tasks is limited by a scarcity of real-world training data, not mechanical engineering. Companies are now deploying capital-intensive "in-field" teams to collect multi-modal data from inside homes, capturing the complexity of mundane human activities to train more capable robots.

The current excitement for consumer humanoid robots mirrors the premature hype cycle of VR in the early 2010s. Robotics experts argue that practical, revenue-generating applications are not in the home but in specific industrial settings like warehouses and factories, where the technology is already commercially viable.

The dream of a do-everything humanoid is a top-down approach that will take a long time. Roboticist Ken Goldberg argues for a bottom-up strategy: master specific, valuable tasks like folding clothes or making coffee reliably first. General intelligence will emerge from combining these skills over time.

The adoption of humanoid robots will mirror that of autonomous vehicles: focus on achievable, single-task applications first. Instead of a complex, general-purpose home robot, the market will first embrace robots trained for specific, repeatable industrial tasks like warehouse logistics or shelf stocking.

While 2025 saw major advancements for robots in commercial settings like autonomous driving (Waymo) and logistics (Amazon), consumer-facing humanoid robots remain impractical. They lack the fine motor skills and dexterity required for complex household chores, failing the metaphorical "laundry test."

The founder of robotics OS Lightberry argues that the industry's "ChatGPT moment" won't be when a robot can fold laundry. Instead, it will be when robots are commonly seen interacting with people in public roles—as shop assistants, event staff, or security—achieving social acceptance first.

Brett Adcock states that Figure AI's "Helix 2" neural net provides the right technical stack for general robotics. The biggest remaining obstacle is not hardware but the immense data required to train the robot for a wide distribution of tasks. The company plans to spend nine figures on data acquisition in 2026 to solve this.